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Identifying, Score Predicting, and Investigating the Factors Affecting Green Restaurants' Customers' Satisfaction

Shah Hosseini, Mansour | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 56332 (44)
  4. University: Sharif University of Technology
  5. Department: Management and Economics
  6. Advisor(s): Khalili Nasr, Arash
  7. Abstract:
  8. Like other industries and research literature, tourism and hospitality have increasingly interpreted and analyzed the vast amount of user-generated content and online reviews to address practical and theoretical issues concerning customer experience and satisfaction. One of the most underdeveloped data-driven research streams in tourism and hospitality is green restaurant research. To address the gap regarding the actual behaviors, perceptions, and anticipations of such restaurants' customers, this study first used a new advanced topic modeling algorithm to identify hidden topics of customer reviews. The result suggests that customers mostly perceive food-related green attributes when it comes to the green aspects of such restaurants. Some found topics, such as food presentation and dress code, were not found in previous green restaurant studies, and some found topics, such as kid friendly and pet friendly, were among the new topics discovered by this study and, to the best of our knowledge were not addressed in the previous restaurant studies. Then, this study extracted the sentiment features for each aspect of the reviews via transfer learning and used aspects and their corresponding sentiments to explore the drivers of green restaurant customer satisfaction. The result shows that when customers are aware of green practices and write about all the aspects in the reviews, green attributes, after food, service, and value, have more influence than the atmosphere on their satisfaction which is a new finding in the context of green restaurants. Moreover, this study proposes a RoBERTa-CNN deep learning model to address the research gap regarding aspect-based sentiment analysis for five levels of sentiments. This model is able to jointly determine the mentioned aspects of the review with their corresponding sentiments. To the best of our knowledge, this study is one of the few ones that address a five-level aspect-based sentiment analysis task. The results show that the proposed model outperforms the previous study in both tasks of aspect detection and aspect category sentiment analysis
  9. Keywords:
  10. Customer Satisfaction ; User Generated Content ; Topic Modeling ; Aspect Based Sentiment Analysis ; Mining User Reviews ; User Online Reviews ; Green Marketing ; Green Restaurant

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  • Abstract
  • Contents
  • Tables
  • Figures
  • Abbreviations and Symbols
  • 1. Introduction
    • 1.1 Research background
    • 1.2 Research gaps
    • 1.3 Research contributions
  • 2. Literature review
    • 2.1 Online reviews in marketing and hospitality and tourism literature
    • 2.2 Online reviews in restaurant literature
    • 2.3 Online reviews in green restaurants research
      • Table 2.1 Most related research on green restaurants' customer satisfaction
    • 2.4 Topic modeling methods
      • Equation (1-1): ,-.=,(,=--,-..)-,=--,-...
      • Figure 2.1 LDA generative process (Vayansky & Kumar, 2020)
      • Table 2.2 Advantages and disadvantages of LDA (Egger & Yu, 2022)
      • Table 2.3 Advantages and disadvantages of BERTopic (Egger & Yu, 2022)
    • 2.5 Aspect-based sentiment analysis
      • Figure 2.2 Approach of sentiment analysis (Wankhade et al., 2022)
  • 3. Methodology
    • 3.1 Data gathering
      • Table 3.1 An example of the scraped dataset
    • 3.2 Data cleaning
    • 3.3 Research flowchart and methodologies
      • Figure 3.1 Flowchart of Studies
      • 3.3.1 Study 1 Methodology
      • 3.3.2 Labeling and creating full dataset procedure
      • Figure 3.2 The pretuned model output example
      • Figure 3.3 The complete dataset
      • 3.3.3 Study 2 Methodology
      • Figure 3.4 Base Bert (Khalid et al., 2021)
      • Figure 3.5 CNN for text classification (Nguyen et al., 2019)
      • Figure 3.6 General architecture of dense layers
      • Equation (3-1): ⁡()=,,-.−-,-.+.
      • Equation (3-2): ⁡()=,-,-−.+.
      • Equation (3-3): ⁡()=(,)
      • Figure 3.7 Proposed model
      • Figure 3.8 Distribution of aspect categories
      • Figure 3.9 Focal loss function (Lin, 2017)
      • Equation (3-4): =,-.,--(,-.=,,.-.).
      • Equation (3-5): =, - + .
      • Equation (3-6): =, - + .
      • Equation (3-7): = , ∗ - + .
      • 3.3.4 Study 3 Methodology
      • Equation (3-8): , -. = ,-.+,-.,-.+,-.,-.+, -.,-.+,-.,-.+,-.,-.+,-. ,-.
      • Table 3.2 Summary of past research on restaurants' customers' satisfaction
  • 4. Studies' results
    • 4.1 Study 1: Identifying green restaurants' aspects from online reviews
      • Figure 4.1 Online reviews wordcloud
      • Table 4.1 Extracted topics via BERTopic
    • 4.2 Study 2: Aspect-based sentiment analysis
      • Table 4.2 System accuracy for joint task
      • Table 4.3 Metrics for ACD task
    • 4.3 Study 3: Investigating aspects affecting green restaurants' customers' satisfaction
      • Figure 4.2 Distribution of sentiments and rating in reviews containing all the aspects
      • Table 4.4 Correlation matrix for the first setting
      • Table 4.5 Result of first setting regression
      • Table 4.6 Correlation matrix for the second setting
      • Table 4.7 Result of second setting regression
  • 5. Discussion and implications
  • 6. Limitations and future research
  • 7. References
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